BACKGROUND Monte Carlo simulations provide accurate models of nuclear medicine imaging systems as they can properly account for the full physics of photon transport. The accuracy of the model included in the maximum-likelihood-expectation-maximization (ML-EM) reconstruction limits the overall accuracy of the reconstruction results. In this paper, we present a Monte Carlo-based ML-EM reconstruct...

Accurate system modeling is normally performed through the System Matrix (SM) in Positron Emission Tomography (PET). The SM can be calculated analytically or by Monte Carlo (MC) methods. MC methods model detector response precisely and lead to better image quality when used in a statistical reconstruction method[1]. However,for modern human PET scanners, two problems hinder the use this method....

Bioluminescence imaging plays an important role in the areas of cancer biology, cell biology, gene therapy, and so on. The 2D planar bioluminescent imaging has been transformed into a 3D framework by bioluminescence tomography (BLT) that enables bioluminescent source reconstruction in a mouse using a modality fusion approach. To solve this BLT problem, a geometrical model of the mouse is usuall...

in the present work‎, ‎a new stochastic algorithm is proposed to solve multiple dimensional fredholm integral equations of the second kind‎. ‎the solution of the‎ integral equation is described by the neumann series expansion‎. ‎each term of this expansion can be considered as an expectation which is approximated by a continuous markov chain monte carlo method‎. ‎an algorithm is proposed to sim...

Introduction: In Single Photon Emission Computed Tomography (SPECT), the projection data used for image reconstruction are distorted by several factors, including attenuation and scattering of gamma rays, collimator structure, data acquisition method, organ motion, and washout of radiopharmaceuticals. All these make reconstruction of a quantitative SPECT image very difficult. Simulation of a SP...

Suppose that it is desired to estimate certain parameters associated with a model of an object that is contained within a larger scene and that only indirect measurements of the scene are available. The optimal solution is provided by a Bayesian approach, which is founded on the posterior probability density distribution. The complete Bayesian procedure requires an integration of the posterior ...

Visualization of space dependency of quantities of interest is not typically a task to set for Monte Carlo calculations. Often it is, though, useful and desired. Most commonly, it is done by segmenting the phase space into meshes on a surface, and obtaining estimates in each subregion. In this paper we investigate the possibility of using an orthogonal function basis for visual description of t...

An optimal solution to the problem of making binary dmisione about a local region of a reconstruction is prov; ded by the Bayeeian method. The decieion is made on the baia of the ratio of the poeterior probabilities for the two hypoth~. The full Bayeaian procedure mquiree an integration of the pterior probability over all pataible valuee of the image outaide the local region being analyzed. In ...

An analysis of a passh'e seismic method for subsur­face imaging is presenled. in which ambienl seismic noise is em­ployed as the source of iIIu,;"ination of undergro~nd scal~erers.The imaging algorithm can ,"corporate new data mto the '!'lagein a recursive fashion, which causes image background nOise todiminish over time. Under the assumption of spatially-incoherentambie...